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Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model / Yizhuo Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 227 - 249 Titre : Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model Type de document : Article/Communication Auteurs : Yizhuo Li, Auteur ; Teng Fei, Auteur ; Yingjing Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 227 - 249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] comportement [Termes IGN] détection de visage [Termes IGN] distribution spatiale [Termes IGN] données environnementales [Termes IGN] émotion [Termes IGN] entropie [Termes IGN] psychologie [Termes IGN] reconnaissance faciale [Termes IGN] sciences humaines [Termes IGN] vision Résumé : (auteur) Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content. Numéro de notice : A2021-037 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1755040 Date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.1080/13658816.2020.1755040 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96746 [article]

Real-time accurate 3D head tracking and pose estimation with consumer RGB-D cameras / David Joseph Tan in International journal of computer vision, vol 126 n° 2-4 (April 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 2-4 (April 2018) . - pp 158 - 183 Titre : Real-time accurate 3D head tracking and pose estimation with consumer RGB-D cameras Type de document : Article/Communication Auteurs : David Joseph Tan, Auteur ; Federico Tombari, Auteur ; Nassir Navab, Auteur Année de publication : 2018 Article en page(s) : pp 158 - 183 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection de visage [Termes IGN] données localisées 3D [Termes IGN] estimation de pose [Termes IGN] image RVB [Termes IGN] méthode robuste [Termes IGN] séquence d'images [Termes IGN] temps réel Résumé : (Auteur) We demonstrate how 3D head tracking and pose estimation can be effectively and efficiently achieved from noisy RGB-D sequences. Our proposal leverages on a random forest framework, designed to regress the 3D head pose at every frame in a temporal tracking manner. One peculiarity of the algorithm is that it exploits together (1) a generic training dataset of 3D head models, which is learned once offline; and, (2) an online refinement with subject-specific 3D data, which aims for the tracker to withstand slight facial deformations and to adapt its forest to the specific characteristics of an individual subject. The combination of these works allows our algorithm to be robust even under extreme poses, where the user’s face is no longer visible on the image. Finally, we also propose another solution that utilizes a multi-camera system such that the data simultaneously acquired from multiple RGB-D sensors helps the tracker to handle challenging conditions that affect a subset of the cameras. Notably, the proposed multi-camera frameworks yields a real-time performance of approximately 8 ms per frame given six cameras and one CPU core, and scales up linearly to 30 fps with 25 cameras. Numéro de notice : A2018-406 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-0988-8 Date de publication en ligne : 02/02/2017 En ligne : https://doi.org/10.1007/s11263-017-0988-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90879 [article]

Single Image Super-Resolution based on Neural Networks for text and face recognition / Clément Peyrard (2017)  

Public Titre : Single Image Super-Resolution based on Neural Networks for text and face recognition Type de document : Thèse/HDR Auteurs : Clément Peyrard, Auteur ; Christophe Garcia, Auteur Editeur : Université de Lyon Année de publication : 2017 Autre Editeur : Lyon : Institut National des Sciences Appliquées INSA Lyon Importance : 187 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université de Lyon opérée au sein de INSA de Lyon, discipline : Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] artefact [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de visage [Termes IGN] image à basse résolution [Termes IGN] image à haute résolution [Termes IGN] méthode fondée sur le noyau [Termes IGN] reconnaissance automatique [Termes IGN] reconnaissance de caractères [Termes IGN] reconnaissance de formes Index. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis is focussed on super-resolution (SR) methods for improving automatic recognition system (Optical Character Recognition, face recognition) in realistic contexts. SR methods allow to generate high resolution images from low resolution ones. Unlike upsampling methods such as interpolation, they restore spatial high frequencies and compensate artefacts such as blur or jaggy edges. In particular, example-based approaches learn and model the relationship between low and high resolution spaces via pairs of low and high resolution images. Artificial Neural Networks are among the most efficient systems to address this problem. This work demonstrate the interest of SR methods based on neural networks for improved automatic recognition systems. By adapting the data, it is possible to train such Machine Learning algorithms to produce high-resolution images. Convolutional Neural Networks are especially efficient as they are trained to simultaneously extract relevant non-linear features while learning the mapping between low and high resolution spaces. On document text images, the proposed method improves OCR accuracy by +7.85 points compared with simple interpolation. The creation of an annotated image dataset and the organisation of an international competition (ICDAR2015) highlighted the interest and the relevance of such approaches. Moreover, if a priori knowledge is available, it can be used by a suitable network architecture. For facial images, face features are critical for automatic recognition. A two step method is proposed in which image resolution is first improved, followed by specialised models that focus on the essential features. An off-the-shelf face verification system has its performance improved from +6.91 up to +8.15 points. Finally, to address the variability of real-world low-resolution images, deep neural networks allow to absorb the diversity of the blurring kernels that characterise the low-resolution images. With a single model, high-resolution images are produced with natural image statistics, without any knowledge of the actual observation model of the low-resolution image. Note de contenu : 1- Introduction 2- Definitions and application domains 3- Literature review 4- Text single image super-resolution 5- Face single image super-resolution 6- Blind and robust super-resolution 7- Conclusion Numéro de notice : 25863 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Discipline : Informatique : Lyon 2017 Organisme de stage : LIRIS nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2017LYSEI083 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95506